With advances in network technologies, geographic barriers are hardly a problem now for global communication. Languages, whether written or spoken, are the major tools for cyber communication, and English, with its wide popularity, has been recognized as a global language. For non-native English-speaking people, extensive reading is a common way to improve a person's command of English. English is even taken as a major course in primary schools in many countries where English as a Foreign Language (EFL) is taught, especially East Asia. One of the keys to success in English learning depends on a person's vocabulary volume. Improving the English vocabulary of a learner has thus become a popular research issue in countries where EFL is widely taught. This paper proposes a personalized English article recommending system, which uses accumulated learner profiles to choose appropriate English articles for a learner. It employs fuzzy inference mechanisms and memory cycle updates to help learners improve their English ability in an extensive reading environment. By using fuzzy inferences and personal memory cycle updates, it is possible to find an article best suited for both a learner's ability and her/his need to review vocabulary. By intensively reading articles recommended through the proposed approach, learners comprehend new words quickly and review words that they knew implicitly as well, thereby efficiently improving their vocabulary volume. Analyses of learner achievements have confirmed that the adaptive learning method presented in this study not only enhances the English ability of learners but also helps maintaining their learning interest.